Microsoft Technology & Civic Innovation presents Machine Eatable, a lunchtime conversation at Civic Hall. This monthly series offers a candid discussion led by community leaders on the front lines of data science for civic good.

Please join us for

When Recommendation Systems Go Bad

Machine learning and recommendations systems have changed how we experience the internet, and by extension, many of the products and services we use in our civic and private lives. While the reach and impact of big data and algorithms will continue to grow, how do we ensure that people are treated justly?

As the people that build these systems, we have a social responsibility to consider their effect on humanity, and we should do whatever we can to prevent these models from perpetuating some of the prejudice and bias that exist in our society today.

Evan Estola (@estola), the Data Team Lead at Meetup, will share examples of recommendation systems that have gone wrong across various industries, and what can be done about them. Importantly, Evan will offer concrete technical approaches that can be employed. Evan will also offer arguments data scientists can use to justify making ethical decisions in a field that's obsessed with optimization.

Schedule

You can arrive at 12pm for light lunch and conversation. Feel free to bring your own, too. We'll begin the program at 12:30pm and wrap up by 1:30.

Hosts

Matt Stempeck (@mstem) of Microsoft Technology & Civic Innovation (@MicrosoftNY) and ecosystem architect Jeanne Brooks (@jmfbrooks).

Civic Hall (@CivicHall)

A collaborative innovation center that advances the use of technology for the public good.

Spread the word about this conversation online with the #machineeatable hashtag.

When Recommendation Systems Go Bad, with Evan Estola